
\begin{table}[ht!]
\begin{center}
\begin{tabular}{l c c c}
\toprule
 & Black/White & White/Hispanic & White/Asian \\
\midrule
White                      & $0.70^{*}$  & $-0.20$    & $-0.80$    \\
                           & $(0.33)$    & $(0.33)$   & $(0.63)$   \\
Houston                    & $-0.08$     & $0.28$     & $0.05$     \\
                           & $(0.67)$    & $(1.15)$   & $(1.31)$   \\
King County                & $0.00$      & $0.83$     & $0.70$     \\
                           & $(0.86)$    & $(1.31)$   & $(1.45)$   \\
Los Angeles                & $1.12$      & $1.78$     & $1.66$     \\
                           & $(0.65)$    & $(1.12)$   & $(1.26)$   \\
Orlando                    & $0.47$      & $0.23$     & $0.08$     \\
                           & $(0.72)$    & $(1.31)$   & $(1.40)$   \\
San Jose                   & $0.33$      & $1.00$     & $1.04$     \\
                           & $(0.81)$    & $(1.16)$   & $(1.35)$   \\
Seattle                    & $1.15$      & $2.62^{*}$ & $2.66^{*}$ \\
                           & $(0.81)$    & $(1.26)$   & $(1.34)$   \\
Tucson                     & $1.02$      & $2.96^{*}$ & $2.50$     \\
                           & $(0.71)$    & $(1.18)$   & $(1.31)$   \\
Closest Trauma (10s miles) & $0.31$      & $0.70^{*}$ & $0.66$     \\
                           & $(0.22)$    & $(0.32)$   & $(0.54)$   \\
Intercept                  & $-1.57^{*}$ & $-1.73$    & $-0.97$    \\
                           & $(0.62)$    & $(1.14)$   & $(1.37)$   \\
\midrule
pseudo.r.squared           & $0.07$      & $0.09$     & $0.14$     \\
nobs                       & $715$       & $732$      & $279$      \\
AIC                        & $916.32$    & $931.29$   & $349.27$   \\
BIC                        & $962.04$    & $977.25$   & $385.58$   \\
Log Likelihood             & $-448.16$   & $-455.65$  & $-164.63$  \\
\bottomrule
\multicolumn{4}{l}{\scriptsize{$^{***}p<0.001$; $^{**}p<0.01$; $^{*}p<0.05$}}
\end{tabular}
\caption{Logistic model regression results. Each model is run on a subset of the data to make either a Black/White, Hispanic/White or Asian/White comparison. Cluster-robust standard errors clustered on city.}
\label{table:regression_all_comparisons_logit}
\end{center}
\end{table}
